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Abstract

Traditional Manufacturing Execution Systems (MES) face critical limitations in addressing Industry 4.0 demands for real-time processing, flexible scheduling, and adaptive decision-making, with less than 1% of manufacturing data effectively utilized. This research develops an Artificial Intelligence (AI)-assisted flexible MES framework integrating real-time data visualization, digital twin technology, and distributed intelligence to enable proactive manufacturing management. The system employs Design Science Research (DSR) methodology and implements a microservices architecture using Apache Kafka for message streaming, Flink for real-time processing, and TensorFlow for AI inference, deployed across five production lines with 2,350 sensors and 45 Programmable Logic Controllers (PLCs). Results demonstrate exceptional performance with system throughput reaching 12,500 messages per second, the design target by 25%, average data collection latency below 10 milliseconds, and 99.9% availability over 72-hour continuous operation. Production efficiency improved significantly with 25% increased output, 65.7% reduction in defect rates (from 35,000 to 12,000 Parts Per Million), and 87.5% decrease in changeover time (from 120 to 15 minutes). Overall Equipment Effectiveness (OEE) increased from 60% to 82%, approaching world-class benchmarks (>85%). This research validates distributed intelligence architectures for achieving simultaneous improvements in manufacturing flexibility and efficiency, challenging traditional theoretical trade-offs while providing a practical implementation roadmap for digital transformation in manufacturing enterprises.

Keywords

Manufacturing Execution Systems (MES) Artificial Intelligence (AI) Digital twin Flexible manufacturing Cyber-Physical Systems (CPS) Microservices architecture Distributed stream processing

Article Details

Author Biographies

ChengHsien Tsai, Faculty of Business and Accountancy, Lincoln University College, Malaysia. Wisma Lincoln, 12-18, Jalan SS 6/12, Ss 6, 47301 Petaling Jaya, Selangor, Malaysia

Cheng-Hsien Tsai is currently pursuing a Doctoral degree in Malaysia. His research interests lie in lean management, risk management, and industrial management of automated smart factories. PhD Researcher at Lincoln University College, Malaysia.

Oyyappan Oyyappa, Faculty of Business and Accountancy, Lincoln University College，in Malaysia. Wisma Lincoln, 12-18, Jalan SS 6/12, Ss 6, 47301 Petaling Jaya, Selangor, Malaysia

Prof. Dr. OYYAPPAN DURAIPANDI, is a professor at Lincoln University and the supervising professor of Cheng Hsien Tsai.

How to Cite
Tsai, C., Oyyappa, O., & Abbas Ali, D. . (2025). Research on real-time data display and production management in a digitalized management factory with an artificial intelligence-assisted flexible manufacturing execution system. Future Technology, 5(1), 263–277. Retrieved from https://fupubco.com/futech/article/view/604
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